上传图片预测时出错
ValueError：block1_conv1层的输入0与图层不兼容：输入形状的预期轴-1具有值3，但接收到的输入具有形状[None，224，224，4]
代码处理/图像预处理
def model_predict(img, model):
img = img.resize((224, 224))
# Preprocessing the image
x = image.img_to_array(img)
x = np.true_divide(x, 255)
x = np.expand_dims(x, axis=0)
# Be careful how your trained model deals with the input
# otherwise, it won't make correct prediction!
x = preprocess_input(x, mode='tf')
preds = model.predict(x)
return preds*培训班产出
flow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
Compiling model...
Model has 14716227 parameters
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 224, 224, 3)] 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 224, 224, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 224, 224, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 112, 112, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 112, 112, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 112, 112, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 56, 56, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 56, 56, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 28, 28, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 14, 14, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
tf_op_layer_Mean (TensorFlow [(None, 512)] 0
_________________________________________________________________
dropout (Dropout) (None, 512) 0
_________________________________________________________________
dense (Dense) (None, 3) 1539
======================================
2条答案
按热度按时间djmepvbi1#
如果您想使用批处理训练和测试部分，您一定要检查
ImageDataGenerator。它不仅可以帮助您创建批次，还可以帮助您进行图像增强（使模型健壮的艺术）。它提供了一个参数rescaling和preprocess_function，您可以轻松地将其设置为rescale=1/255., preprocessing_function=preprocess_input,您的代码在通道部分有问题。如果您使用的是
channels_last，那么它已经在(batch,width,height,3)3通道数据上进行了训练，当您试图通过数据进行测试时，它会显示错误。在这种情况下，应删除Alpha通道。请检查ImageData Generator。在所有的
flow_from_dataframe，flow_from_directory和flow中有一个参数作为默认的color_mode='rgb。否则，您可以将图像打开为
然后您可以应用
preprocess_function注意：如果您使用单个图像，则应使用
numpy的reshape或expand_dims方法来创建额外的批处理轴，因为Keras总是期望一个批处理。1wnzp6jl2#
因为你试图用4通道图像来填充模型。请在转换图像到阵列后采取第一个3通道。